1. 相关命令

with tf.name_scope("accuracy"): tf.summary.scalar('accuracy', accuracy)     # Add the accuracy to the summary merged_summary = tf.summary.merge_all()     # Merge all summaries together writer = tf.summary.FileWriter(filewriter_path)     # Initialize the FileWriter

writer.add_graph(sess.graph)  # Add the model graph to TensorBoard

tf.summary.histogram(var.name, var)   # Add the variables we train to the summary

tf.variable_scope(name)

2. tensorboard 存储格式及读取方法

存储文件格式为： events.out.tfevents.1493946419.wgb

我将上述文件存储在文件夹：~/Desktop/model_wgb/finetune_alexnet_with_tensorflow--master/data/filewriter

因此我在ubuntu 命令终端采用的打开命令： wgb@wgb:~/Desktop/model_wgb/finetune_alexnet_with_tensorflow--master/data$ tensorboard --logdir='filewriter/

得：

Starting TensorBoard b'41' on port 6006 (You can navigate to http://127.0.1.1:6006) 将http://127.0.1.1:6006 复制粘贴到IE浏览器中即可

可参考莫烦tensorflow视频14,15

3. 生成tensorboard的例子：

with tf.name_scope("cross_ent"): loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits=score, labels=y)) # Train op with tf.name_scope("train"): # Get gradients of all trainable variables gradients = tf.gradients(loss, var_list) #导数 gradients = list(zip(gradients, var_list)) # Create optimizer and apply gradient descent to the trainable variables optimizer = tf.train.GradientDescentOptimizer(learning_rate) train_op = optimizer.apply_gradients(grads_and_vars=gradients) # Add gradients to summary for gradient, var in gradients: tf.summary.histogram(var.name + '/gradient', gradient) # Add the variables we train to the summary for var in var_list: tf.summary.histogram(var.name, var) # Add the loss to summary tf.summary.scalar('cross_entropy', loss) # Evaluation op: Accuracy of the model with tf.name_scope("accuracy"): # top 1 accuracy # correct_pred = tf.equal(tf.argmax(score, 1), tf.argmax(y, 1)) # accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # TODO top k accuracy labels = tf.argmax(y, 1) # get real label topFiver = tf.nn.in_top_k(score, labels, 1) # check if top N is in real label or not accuracy = tf.reduce_mean(tf.cast(topFiver, tf.float32)) # Add the accuracy to the summary tf.summary.scalar('accuracy', accuracy) # Merge all summaries together merged_summary = tf.summary.merge_all() # Initialize the FileWriter writer = tf.summary.FileWriter(filewriter_path) # Initialize an saver for store model checkpoints saver = tf.train.Saver() # Initalize the data generator seperately for the training and validation set train_generator = ImageDataGenerator( train_file, horizontal_flip=True, shuffle=True) val_generator = ImageDataGenerator(val_file, shuffle=False) # Get the number of training/validation steps per epoch train_batches_per_epoch = np.floor(train_generator.data_size / batch_size).astype(np.int16) val_batches_per_epoch = np.floor(val_generator.data_size / batch_size).astype(np.int16) # Start Tensorflow session with tf.Session() as sess: # Initialize all variables sess.run(tf.global_variables_initializer()) # Add the model graph to TensorBoard writer.add_graph(sess.graph)